Abstract
Asset value predictability remains a major research concern in financial market especially when considering the effect of unprecedented market fluctuations on the behaviour of market participants.
 This paper presents preliminary results toward the building a reliable forward problem on ensemble approach IPCBR model, that leverages the capabilities of Case based Reasoning(CBR) and Inverse Problem Techniques (IPTs) to describe and model abnormal stock market fluctuations (often associated with asset bubbles) using datasets from historical stock market prices. The framework uses a rich set of past observations and geometric pattern description and then applies a CBR to formulate the forward problem, Inverse Problem formulation is then applied to identify a set of parameters that can statistically be associated with the occurrence of the observed patterns.
 This research work presents a formative strategy aimed to determine the causes of behaviour, rather than predict future time series points which brings a novel perspective to the problem of asset bubbles predictability, and a deviation from the existing research trend. The results depict the stock dynamics and statistical fluctuating evidence associated with the envisaged bubble problem.
Highlights
Developments in Artificial Intelligence (AI) and Machine Learning [1]-[3] have revealed numerous spectacular outcomes in diverse fields
We propose an ensemble Inverse Problem Case Base Reasoning (IPCBR) model that uses the simplicity and applicability of CBR to deliver a more robust representation of asset value fluctuation patterns and the successes of the Inverse Problem to identify the factors that most likely cause such patterns
Many inverse problems exist; [26] classified various approaches to solving inverse problems into three main categories namely, (i) Regularization of Ill-Posed Problems, (ii) Stochastic or Bayesian Inversion [29], and (iii) Functional analysis, a decision making approach in which a problem is brokendown into its component functions, which are further divided into sub-functions until the function level suitable for solving the problem is reached
Summary
Developments in Artificial Intelligence (AI) and Machine Learning [1]-[3] have revealed numerous spectacular outcomes in diverse fields. We propose an ensemble Inverse Problem Case Base Reasoning (IPCBR) model that uses the simplicity and applicability of CBR to deliver a more robust representation of asset value fluctuation patterns (and their subsequent classification as potential asset bubbles) and the successes of the Inverse Problem to identify the factors that most likely cause such patterns. The outcome of this will be used as a case base for standard Case-based Reasoning process and will be evaluated against some known episodic data retrieved from the Yahoo Finance and human expert advice. The paper closes with a critical discussion on the major contributions this work intends to deliver, and a set of relevant concluding observations
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